DBMS > Microsoft Access vs. Microsoft Azure Data Explorer vs. Milvus vs. Snowflake

System Properties Comparison Microsoft Access vs. Microsoft Azure Data Explorer vs. Milvus vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Fully managed big data interactive analytics platform

A DBMS designed for efficient storage of vector data and vector similarity searches

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

column oriented

Vector DBMS

Relational DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/services/data-explorer

milvus.io

www.snowflake.com

Technical documentation

developer.microsoft.com/en-us/access

docs.microsoft.com/en-us/azure/data-explorer

milvus.io/docs/overview.md

docs.snowflake.net/manuals/index.html

Developer

Microsoft

Snowflake Computing Inc.

Initial release

1992

2019

2014

Current release

1902 (16.0.11328.20222), March 2019

cloud service with continuous releases

2.3.4, January 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Server operating systems

Windows

Not a real database server, but making use of DLLs

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

hosted

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

Vector, Numeric and String

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

but not compliant to any SQL standard

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

ADO.NET
DAO
ODBC
OLE DB

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C++
Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

Yes, possible languages: KQL, Python, R

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

a simple user-level security was built in till version Access 2003

Azure Active Directory Authentication

Role based access control and fine grained access rights

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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